A Study on Derivational Affixes of Indonesian Noun-Formation in Newspaper Editorial: A Semantic Perspective
Bibliographic record
Abstract
This study aimed at investigating the types of derivational affixes of Indonesian noun-formation in newspaper editorial of kompas. Kompas newspaper is wide circulation or it has a tremendous reading circulation in Indonesia. This study used a descriptive qualitative method by using the theory of structural linguistics to interprete the grammatical meaning carried out in the process of derivational affixes of Indonesian noun-formation. The method of analysis data applied distributional method in terms of classifyng lexical category of Indonesian derived nouns producing affixation. The theory referring to the patterns of derivation and structure, which was developed by Aronoff & Fudeman (2005). The object of the study is a derivational affixes of Indonesian noun-formation that exist in newspaper editorial of Indonesian kompas. Based on the analysis of the data the findings showing that there are 7 types of derivational affixes of Indonesian noun-formation exists in the newspaper editorial (tajuk rencana) of kompas namely (1) suffix -an, (2) infix -em- + suffix -an, (3) infix -el-, (4) confix per-an, (5) confix ke-an, (6) confix pe-an and (7) confix pen-an. These types are considered important viewed from the morphology study in the field of linguistics. This is to say that the phenomena referring to the point of the function of the language is considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".